8.6.1. Massively univariate analysis of a calculation task from the Localizer datasetΒΆ

This example shows how to use the Localizer dataset in a basic analysis. A standard Anova is performed (massively univariate F-test) and the resulting Bonferroni-corrected p-values are plotted. We use a calculation task and 20 subjects out of the 94 available.

The Localizer dataset contains many contrasts and subject-related variates. The user can refer to the plot_localizer_mass_univariate_methods.py example to see how to use these.

```# Author: Virgile Fritsch, <virgile.fritsch@inria.fr>, May. 2014
import numpy as np
import matplotlib.pyplot as plt
from nilearn import datasets
```

```n_samples = 20
n_subjects=n_samples)
tested_var = np.ones((n_samples, 1))
```

```nifti_masker = NiftiMasker(
smoothing_fwhm=5,
memory='nilearn_cache', memory_level=1)  # cache options
cmap_filenames = localizer_dataset.cmaps
```

Anova (parametric F-scores)

```from sklearn.feature_selection import f_regression
center=False)  # do not remove intercept
pvals_anova[np.isnan(pvals_anova)] = 1
pvals_anova[pvals_anova > 1] = 1
neg_log_pvals_anova = - np.log10(pvals_anova)
neg_log_pvals_anova)
```

Visualization

```from nilearn.plotting import plot_stat_map, show

# Various plotting parameters
z_slice = 45  # plotted slice

threshold = - np.log10(0.1)  # 10% corrected

# Plot Anova p-values
fig = plt.figure(figsize=(5, 6), facecolor='w')
threshold=threshold,
display_mode='z', cut_coords=[z_slice],
figure=fig)

threshold)

title = ('Negative \$\log_{10}\$ p-values'
'\n(Parametric + Bonferroni correction)'